code: def _groups_format(groups: torch.Tensor) -> torch.Tensor:
      """Reshape groups to correspond to preds and target."""
      return groups.reshape(groups.shape[0], -1)
  
file_path: torchmetrics/src/torchmetrics/functional/classification/group_fairness.py
imports: from typing import Dict, List, Optional, Tuple
  import torch
  from typing_extensions import Literal
  from torchmetrics.functional.classification.stat_scores import _binary_stat_scores_arg_validation, _binary_stat_scores_format, _binary_stat_scores_tensor_validation, _binary_stat_scores_update
  from torchmetrics.utilities import rank_zero_warn
  from torchmetrics.utilities.compute import _safe_divide
  from torchmetrics.utilities.data import _flexible_bincount
label_desc: Use image node_id 2 for calling a global function with example usage: _groups_format(groups) and returns: groups